Community Detection in Hypergraphen

Viele Datensätze können als Graphen aufgefasst werden, d.h. als Elemente (Knoten) und binäre Verbindungen zwischen ihnen (Kanten). Unter dem Begriff der "Complex Network Analysis" sammeln sich eine ganze Reihe von Verfahren, die die Untersuchung von Datensätzen allein aufgrund solcher struktureller Eigenschaften erlauben. "Community Detection" als Untergebiet beschäftigt sich mit der Identifikation besonders stark vernetzter Teilgraphen. Über den Nutzen hinaus, den eine Gruppierung verwandter Element direkt mit sich bringt, können derartige Gruppen zu einzelnen Knoten zusammengefasst werden, was einen neuen Graphen von reduzierter Komplexität hervorbringt, der die Makrostruktur des ursprünglichen Graphen unter Umständen besser hervortreten lässt. Fortschritte im Bereich der "Community Detection" verbessern daher auch das Verständnis komplexer Netzwerke im allgemeinen. Nicht jeder Datensatz lässt sich jedoch angemessen mit binären Relationen darstellen - Relationen höherer Ordnung führen zu sog. Hypergraphen. Gegenstand dieser Arbeit ist die Verallgemeinerung von Ansätzen zur "Community Detection" auf derartige Hypergraphen. Im Zentrum der Aufmerksamkeit stehen dabei "Social Bookmarking"-Datensätze, wie sie von Benutzern von "Bookmarking"-Diensten erzeugt werden. Dabei ordnen Benutzer Dokumenten frei gewählte Stichworte, sog. "Tags" zu. Dieses "Tagging" erzeugt, für jede Tag-Zuordnung, eine ternäre Verbindung zwischen Benutzer, Dokument und Tag, was zu Strukturen führt, die 3-partite, 3-uniforme (im folgenden 3,3-, oder allgemeiner k,k-) Hypergraphen genannt werden. Die Frage, der diese Arbeit nachgeht, ist wie diese Strukturen formal angemessen in "Communities" unterteilt werden können, und wie dies das Verständnis dieser Datensätze erleichtert, die potenziell sehr reich an latenten Informationen sind. Zunächst wird eine Verallgemeinerung der verbundenen Komponenten für k,k-Hypergraphen eingeführt. Die normale Definition verbundener Komponenten weist auf den untersuchten Datensätzen, recht uninformativ, alle Elemente einer einzelnen Riesenkomponente zu. Die verallgemeinerten, so genannten hyper-inzidenten verbundenen Komponenten hingegen zeigen auf den "Social Bookmarking"-Datensätzen eine charakteristische Größenverteilung, die jedoch bspw. von Spam-Verhalten zerstört wird - was eine Verbindung zwischen Verhaltensmustern und strukturellen Eigenschaften zeigt, der im folgenden weiter nachgegangen wird. Als nächstes wird das allgemeine Thema der "Community Detection" auf k,k-Hypergraphen eingeführt. Drei Herausforderungen werden definiert, die mit der naiven Anwendung bestehender Verfahren nicht gemeistert werden können. Außerdem werden drei Familien synthetischer Hypergraphen mit "Community"-Strukturen von steigender Komplexität eingeführt, die prototypisch für Situationen stehen, die ein erfolgreicher Detektionsansatz rekonstruieren können sollte. Der zentrale methodische Beitrag dieser Arbeit besteht aus der im folgenden dargestellten Entwicklung eines multipartiten (d.h. für k,k-Hypergraphen geeigneten) Verfahrens zur Erkennung von "Communities". Es basiert auf der Optimierung von Modularität, einem etablierten Verfahrung zur Erkennung von "Communities" auf nicht-partiten, d.h. "normalen" Graphen. Ausgehend vom einfachst möglichen Ansatz wird das Verfahren iterativ verfeinert, um den zuvor definierten sowie neuen, in der Praxis aufgetretenen Herausforderungen zu begegnen. Am Ende steht die Definition der "ausgeglichenen multi-partiten Modularität". Schließlich wird ein interaktives Werkzeug zur Untersuchung der so gewonnenen "Community"-Zuordnungen vorgestellt. Mithilfe dieses Werkzeugs können die Vorteile der zuvor eingeführten Modularität demonstriert werden: So können komplexe Zusammenhänge beobachtet werden, die den einfacheren Verfahren entgehen. Diese Ergebnisse werden von einer stärker quantitativ angelegten Untersuchung bestätigt: Unüberwachte Qualitätsmaße, die bspw. den Kompressionsgrad berücksichtigen, können über eine größere Menge von Beispielen die Vorteile der ausgeglichenen multi-partiten Modularität gegenüber den anderen Verfahren belegen. Zusammenfassend lassen sich die Ergebnisse dieser Arbeit in zwei Bereiche einteilen: Auf der praktischen Seite werden Werkzeuge zur Erforschung von "Social Bookmarking"-Daten bereitgestellt. Demgegenüber stehen theoretische Beiträge, die für Graphen etablierte Konzepte - verbundene Komponenten und "Community Detection" - auf k,k-Hypergraphen übertragen.Many datasets can be interpreted as graphs, i.e. as elements (nodes) and binary relations between them (edges). Under the label of complex network analysis, a vast array of graph-based methods allows the exploration of datasets purely based on such structural properties. Community detection, as a subfield of network analysis, aims to identify well-connected subparts of graphs. While the grouping of related elements is useful in itself, these groups can furthermore be collapsed into single nodes, creating a new graph of reduced complexity which may better reveal the original graph's macrostructure. Therefore, advances in community detection improve the understanding of complex networks in general. However, not every dataset can be modelled properly with binary relations - higher-order relations give rise to so-called hypergraphs. This thesis explores the generalization of community detection approaches to hypergraphs. In the focus of attention are social bookmarking datasets, created by users of online bookmarking services who assign freely chosen keywords, so-called "tags", to documents. This "tagging" creates, for each tag assignment, a ternary connection between the user, the document, and the tag, inducing particular structures called 3-partite, 3-uniform hypergraphs (henceforth called 3,3- or more generally k,k-hypergraphs). The question pursued here is how to decompose these structures in a formally adequate manner, and how this improves the understanding of these rich datasets. First, a generalization of connected components to k,k-hypergraphs is proposed. The standard definition of connected components here rather uninformatively assigns almost all elements to a single giant component. The generalized so-called hyperincident connected components, however, show a characteristic size distribution on the social bookmarking datasets that is disrupted by, e.g., spamming activity - demonstrating a link between behavioural patterns and structural features that is further explored in the following. Next, the general topic of community detection in k,k-hypergraphs is introduced. Three challenges are posited that are not met by the naive application of standard techniques, and three families of synthetic hypergraphs are introduced containing increasingly complex community setups that a successful detection approach must be able to identify. The main methodical contribution of this thesis consists of the following development of a multi-partite (i.e. suitable for k,k-hypergraphs) community detection algorithm. It is based on modularity optimization, a well-established algorithm to detect communities in non-partite, i.e. "normal" graphs. Starting from the simplest approach possible, the method is successively refined to meet the previously defined as well as empirically encountered challenges, culminating in the definition of the "balanced multi-partite modularity". Finally, an interactive tool for exploring the obtained community assignments is introduced. Using this tool, the benefits of balanced multi-partite modularity can be shown: Intricate patters can be observed that are missed by the simpler approaches. These findings are confirmed by a more quantitative examination: Unsupervised quality measures considering, e.g., compression document the advantages of this approach on a larger number of samples. To conclude, the contributions of this thesis are twofold. It provides practical tools for the analysis of social bookmarking data, complemented with theoretical contributions, the generalization of connected components and modularity from graphs to k,k-hypergraphs

THE RELATIONSHIP BETWEEN DELIBERATE PRACTICE AND READING ABILITY

Many students are not prepared to meet the literacy demands of college and career as defined by the Common Core State Standards (2010). Literacy researchers have struggled to define the frequency and type of reading practice necessary to nurture the development of reading ability. The principles of deliberate practice provide a theoretical framework that could describe the type of practice necessary to develop expertise in reading. The purpose of this study was to explore the relationship between deliberate practice and reading ability. In this study, an educational technology, Learning Oasis, was used to deliver deliberate practice and monitor change in student reading ability over time. The hypotheses were that participants that engaged in more deliberate practice, as operationalized in this study, would experience more rapid growth and achieve higher levels of reading ability. Participants in this study (N = 1,369) ranged from grades one through twelve and were from a suburban school district in Mississippi. Each participant had at least three measurement occasions separated by at least three months each. The Lexile Framework for Reading was used to estimate participant reading ability during this research. Given the longitudinal nature of the data, a multilevel model was used to explore individual change over time. A negative exponential functional form was determined to best model change in participant reading ability over time. The results showed that, on average, participants that engaged in more deliberate practice (i.e., targeted practice with immediate feedback completed intensely over a long period of time) grew more rapidly and reached a higher ability level than participants that completed less deliberate practice. Implications for educators, educational technology designers, and researchers are discussed along with potential future areas of research.Doctor of Philosoph

Individual differences in navigating and experiencing presence in virtual environments

The effort of making Virtual Environments (VEs) more useful and satisfactory to use lie at the core of usability research. Because of their development and widespread accessibility, VEs are being used by an ever-increasing diversity of users, whose individual differences impact on both task performance and level of satisfaction. This aspect raises a major challenge in terms of designing adaptive VEs, suitable not for the average user but for each individual user. One way to address this challenge is through the study of individual differences and their implications, which should lead to new effective ways to accommodate them. Adaptivity reflects the system’s capability to automatically tailor itself to dynamically changing user behaviour. This capability is enabled by a user model, acquired on the basis of identifying the user’s patterns of behaviour. This thesis addresses the issue of studying and accommodating individual differences with the purpose of designing adaptive VEs. The individual differences chosen to be investigated are those that impact particularly on two fundamental aspects underlying each interaction with a VE, namely navigation and sense of presence. Both these aspects are related to the perceived usability of VEs. The impact that a set of factors like empathy, absorption, creative imagination and willingness to be transported within the virtual world has on presence has been investigated and described through a prediction equation. Based on these findings, a set of guidelines has been developed for designing VEs able to accommodate these individual differences in order to support users to experience a higher level of presence. The individual differences related to navigation within VE have been investigated in the light of discriminating between efficient versus inefficient search strategies. Building a user model of navigation affords not only a better understanding of user spatial behaviour, but also supports the development of an adaptive VE which could help low spatial users to improve their navigational skills by teaching them the efficient navigational rules and strategies

TOWARDS A MODEL FOR ARTIFICIAL AESTHETICS - Contributions to the Study of Creative Practices in Procedural and Computational Systems

Este trabalho propõe o desenvolvimento de um modelo analítico e da terminologia a ele associada para o estudo de artefactos estéticos computacionais. Reconhecendo a presença e uso crescentes dos media computacionais, começamos por estudar como através da remediação eles transformam quantitativamente os media precedentes, e como as suas propriedades procedimentais e computacionais os afectam qualitativamente. Para perceber o potencial criativo e a especificidade dos media computacionais, desenvolvemos um modelo para a sua prática, crítica e análise. Como ponto de partida recorremos à tipologia desenvolvida por Espen Aarseth para o estudo de cibertextos, avaliando a sua adequação à análise de peças ergódicas visuais e audiovisuais, adaptando-a e expandindo-a com novas variáveis e respectivos valores. O modelo é testado através da análise de um conjunto de peças que representam diversas abordagens à criação procedimental e diversas áreas de actividade criativa contemporânea. É posteriormente desenvolvida uma análise de controlo para avaliar a usabilidade e utilidade do modelo, a sua capacidade para a elaboração de classificações objectivas e o rigor da análise. Demonstramos a adequação parcial do modelo de Aarseth para o estudo de artefactos não textuais e expandimo-lo para melhor descrever as peças estudadas. Concluímos que o modelo apresentado produz boas descrições das peças, agrupando-as logicamente, reflectindo afinidades estilísticas e procedimentais entre sistemas que, se estudados com base nas suas propriedades sensoriais ou nas suas estruturas de superfície provavelmente não revelariam muitas semelhanças. As afinidades reveladas pelo modelo são estruturais e procedimentais, e atestam a importância das características computacionais para a apreciação estética das obras. Verificamos a nossa conjectura inicial sobre a importância da procedimentalidade não só nas fases de desenvolvimento e implementação das obras mas também como base conceptual e estética na criação e apreciação artísticas, como um prazer estético

Collaborative Knowledge Visualisation for Cross-Community Knowledge Exchange

The notion of communities as informal social networks based on shared interests or common practices has been increasingly used as an important unit of analysis when considering the processes of cooperative creation and sharing of knowledge. While knowledge exchange within communities has been extensively researched, different studies observed the importance of cross-community knowledge exchange for the creation of new knowledge and innovation in knowledge-intensive organizations. Especially in knowledge management a critical problem has become the need to support the cooperation and exchange of knowledge between different communities with highly specialized expertise and activities. Though several studies discuss the importance and difficulties of knowledge sharing across community boundaries, the development of technological support incorporating these findings has been little addressed. This work presents an approach to supporting cross-community knowledge exchange based on using knowledge visualisation for facilitating information access in unfamiliar community domains. The theoretical grounding and practical relevance of the proposed approach are ensured by defining a requirements model that integrates theoretical frameworks for cross-community knowledge exchange with practical needs of typical knowledge management processes and sensemaking tasks in information access in unfamiliar domains. This synthesis suggests that visualising knowledge structures of communities and supporting the discovery of relationships between them during access to community spaces, could provide valuable support for cross-community discovery and sharing of knowledge. This is the main hypothesis investigated in this thesis. Accordingly, a novel method is developed for eliciting and visualising implicit knowledge structures of individuals and communities in form of dynamic knowledge maps that make the elicited knowledge usable for semantic exploration and navigation of community spaces. The method allows unobtrusive construction of personal and community knowledge maps based on user interaction with information and their use for dynamic classification of information from a specific point of view. The visualisation model combines Document Maps presenting main topics, document clusters and relationships between knowledge reflected in community spaces with Concept Maps visualising personal and shared conceptual structures of community members. The technical realization integrates Kohonen’s self-organizing maps with extraction of word categories from texts, collaborative indexing and personalised classification based on user-induced templates. This is accompanied by intuitive visualisation and interaction with complex information spaces based on multi-view navigation of document landscapes and concept networks. The developed method is prototypically implemented in form of an application framework, a concrete system and a visual information interface for multi-perspective access to community information spaces, the Knowledge Explorer. The application framework implements services for generating and using personal and community knowledge maps to support explicit and implicit knowledge exchange between members of different communities. The Knowledge Explorer allows simultaneous visualisation of different personal and community knowledge structures and enables their use for structuring, exploring and navigating community information spaces from different points of view. The empirical evaluation in a comparative laboratory study confirms the adequacy of the developed solutions with respect to specific requirements of the cross-community problem and demonstrates much better quality of knowledge access compared to a standard information seeking reference system. The developed evaluation framework and operative measures for quality of knowledge access in cross-community contexts also provide a theoretically grounded and practically feasible method for further developing and evaluating new solutions addressing this important but little investigated problem